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As the use of black-box models becomes ubiquitous in high stake decision-making systems, demands for fair and interpretable models are increasing.
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Three naive bayes approaches for discrimination-free classification
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Finding a short and accurate decision rule in disjunctive normal form by exhaustive search
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Fairness-aware learning through regularization approach
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A hierarchical model for association rule mining of sequential events: An approach to automated medical symptom prediction
McCormick, T., Rudin, C., and Madigan, D · 2011
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Dwork, C., Hardt, M., Pitassi, T., Reingold, O., and Zemel, R · 2012
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Kamiran, F. and Calders, T · 2012
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Kamishima, T., Akaho, S., Asoh, H., and Sakuma, J · 2012
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Credit risk scorecards: developing and implementing intelligent credit scoring , volume 3
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Can an algorithm hire better than a human?, Jun 2015
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Falling rule lists
Wang, F. and Rudin, C · 2015
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Zliobaite, I · 2015
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Machine bias
Angwin, J., Larson, J., Mattu, S., and Kirchner, L · 2016
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Equality of opportunity in supervised learning
Hardt, M., Price, E., Srebro, N., et al · 2016
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Fairness in learning: Classic and contextual bandits
Joseph, M., Kearns, M., Morgenstern, J. H., and Roth, A · 2016
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Rationalizing neural predictions
Lei, T., Barzilay, R., and Jaakkola, T · 2016
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Why should i trust you?: Explaining the predictions of any classifier
Ribeiro, M. T., Singh, S., and Guestrin, C · 2016
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Grad-cam: Why did you say that?
Selvaraju, R. R., Das, A., Vedantam, R., Cogswell, M., Parikh, D., and Batra, D · 2016
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Dash, S., Gunluk, O., and Wei, D · 2018
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A survey of methods for explaining black box models
Guidotti, R., Monreale, A., Ruggieri, S., Turini, F., Giannotti, F., and Pedreschi, D · 2018
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The mythos of model interpretability
Lipton, Z. C · 2018
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Learning adversarially fair and transferable representations
Madras, D., Creager, E., Pitassi, T., and Zemel, R · 2018
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Methods for interpreting and understanding deep neural networks
Montavon, G., Samek, W., and Müller, K.-R · 2018
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Fair inference on outcomes
Nabi, R. and Shpitser, I · 2018
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Supersparse linear integer models for optimized medical scoring systems
Ustun, B. and Rudin, C · 2016
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Feature importance measure for non-linear learning algorithms
Vidovic, M. M.-C., Görnitz, N., Müller, K.-R., and Kloft, M · 2016
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Angelino, E., Larus-Stone, N., Alabi, D., Seltzer, M., and Rudin, C · 2017
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Classification and regression trees
Breiman, L · 2017
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Optimized pre-processing for discrimination prevention
Calmon, F., Wei, D., Vinzamuri, B., Ramamurthy, K. N., and Varshney, K. R · 2017
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Fair prediction with disparate impact: A study of bias in recidivism prediction instruments
Chouldechova, A · 2017
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Translation tutorial: 21 fairness definitions and their politics
Narayanan, A · 2018
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Learning optimal decision trees with sat
Narodytska, N., Ignatiev, A., Pereira, F., Marques-Silva, J., and RAS, I · 2018
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Fair forests: Regularized tree induction to minimize model bias
Raff, E., Sylvester, J., and Mills, S · 2018
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Please stop explaining black box models for high stakes decisions
Rudin, C · 2018
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Fairness definitions explained
Verma, S. and Rubin, J · 2018
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Fairwashing: the risk of rationalization
Aïvodji, U., Arai, H., Fortineau, O., Gambs, S., Hara, S., and Tapp, A · 2019
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Classification with fairness constraints: A meta-algorithm with provable guarantees
Celis, L. E., Huang, L., Keswani, V., and Vishnoi, N. K · 2019
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Explanations can be manipulated and geometry is to blame
Dombrowski, A.-K., Alber, M., Anders, C. J., Ackermann, M., Müller, K.-R., and Kessel, P · 2019
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A comparative study of fairness-enhancing interventions in machine learning
Friedler, S. A., Scheidegger, C., Venkatasubramanian, S., Choudhary, S., Hamilton, E. P., and Roth, D · 2019
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Pretending fair decisions via stealthily biased sampling
Fukuchi, K., Hara, S., and Maehara, T · 2019
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Fooling neural network interpretations via adversarial model manipulation
Heo, J., Joo, S., and Moon, T · 2019
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” how do i fool you?”: Manipulating user trust via misleading black box explanations
Lakkaraju, H. and Bastani, O · 2019
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The dangers of post-hoc interpretability: unjustified counterfactual explanations
Laugel, T., Lesot, M.-J., Marsala, C., Renard, X., and Detyniecki, M · 2019
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The bouncer problem: Challenges to remote explainability
Merrer, E. L. and Tredan, G · 2019
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Explaining machine learning classifiers through diverse counterfactual explanations
Mothilal, R. K., Sharma, A., and Tan, C · 2019
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Rafique, H., Wang, T., and Lin, Q · 2019
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How can we fool lime and shap? adversarial attacks on post hoc explanation methods
Slack, D., Hilgard, S., Jia, E., Singh, S., and Lakkaraju, H · 2019
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Gaining free or low-cost interpretability with interpretable partial substitute
Wang, T · 2019
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Fairness constraints: A flexible approach for fair classification
Zafar, M. B., Valera, I., Gomez-Rodriguez, M., and Gummadi, K. P · 2019
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Faht: An adaptive fairness-aware decision tree classifier
Zhang, W. and Ntoutsi, E · 2019
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Explainable artificial intelligence (xai): Concepts, taxonomies, opportunities and challenges toward responsible ai
Arrieta, A. B., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., García, S., Gil-López, S., Molina, D., Benjamins, R., et al · 2020
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